2 citations · 4 across the 10 of their papers we have counts for
11 papers
Hitting Time Isomorphism for Multi-Stage Planning with Foundation Policies
Magnus Victor Boock, Abdullah Akgül, Mustafa Mert Çelikok +1
We present a new operator-theoretic representation learning framework for offline reinforcement learning that recovers the directed temporal geometry of a controlled Markov process…
Distributional Active Inference
Abdullah Akgül, Gulcin Baykal, Manuel Haußmann +2
Optimal control of complex environments with robotic systems faces two complementary and intertwined challenges: efficient organization of sensory state information and far-sighted…
ObjectRL: An Object-Oriented Reinforcement Learning Codebase
Gulcin Baykal, Abdullah Akgül, Manuel Haussmann +4
ObjectRL is an open-source Python codebase for deep reinforcement learning (RL), designed for research-oriented prototyping with minimal programming effort. Unlike existing codebas…
Overcoming Non-stationary Dynamics with Evidential Proximal Policy Optimization
Abdullah Akgül, Gulcin Baykal, Manuel Haußmann +1
Continuous control of non-stationary environments is a major challenge for deep reinforcement learning algorithms. The time-dependency of the state transition dynamics aggravates t…
Deterministic Uncertainty Propagation for Improved Model-Based Offline Reinforcement Learning
Abdullah Akgül, Manuel Haußmann, Melih Kandemir
Current approaches to model-based offline reinforcement learning often incorporate uncertainty-based reward penalization to address the distributional shift problem. These approach…
Calibrating Bayesian UNet++ for Sub-Seasonal Forecasting
Busra Asan, Abdullah Akgül, Alper Unal +2
Seasonal forecasting is a crucial task when it comes to detecting the extreme heat and colds that occur due to climate change. Confidence in the predictions should be reliable sinc…
Weighted Sequential Bayesian Inference for Non-Stationary Linear Contextual Bandits
Nicklas Werge, Yi-Shan Wu, Abdullah Akgül +1
In non-stationary linear contextual bandits, existing efficient algorithms typically rely on the Weighted Regularized Least-Squares (WRLS) estimator. Because WRLS only provides poi…
PAC-Bayesian Soft Actor-Critic Learning
Bahareh Tasdighi, Abdullah Akgül, Manuel Haussmann +2
Actor-critic algorithms address the dual goals of reinforcement learning (RL), policy evaluation and improvement via two separate function approximators. The practicality of this a…
How to Combine Variational Bayesian Networks in Federated Learning
Atahan Ozer, Kadir Burak Buldu, Abdullah Akgül +1
Federated Learning enables multiple data centers to train a central model collaboratively without exposing any confidential data. Even though deterministic models are capable of pe…
Continual Learning of Multi-modal Dynamics with External Memory
Abdullah Akgül, Gozde Unal, Melih Kandemir
We study the problem of fitting a model to a dynamical environment when new modes of behavior emerge sequentially. The learning model is aware when a new mode appears, but it canno…
Evidential Turing Processes
Melih Kandemir, Abdullah Akgül, Manuel Haussmann +1
A probabilistic classifier with reliable predictive uncertainties i) fits successfully to the target domain data, ii) provides calibrated class probabilities in difficult regions o…